Many interactive learning systems have advantageously been developed for education and training. Such interactive programs: (1) are easily reproduced and distributed; (2) can be worked on at the student's or learner's own pace; (3) permit non-linear exploration of the available material; (4) can automatically provide immediate feedback for learners' actions and decisions; and (5) allow response attempts in private without fear of embarrassment.
Recently, many education and cognitive science researchers have argued for the superior results of “problem-based” educational approaches, where students are given rich problem-solving scenarios rather than static, expository material or simple drills. Interactive computer-based implementations of this problem-based approach have generally been referred to in the market as “simulations.” There are several different kinds of educational simulations.
The vast majority of these simulations are “branching” simulations, where all possible states within the scenario have been predefined, and branching from one state to another depends upon the actions and choices made by the student. The branching may either be hard-coded in the software or may use a more generalized, data-driven branching engine.
Another type of educational simulation is comprised of a quantitative domain model that is executed dynamically and iteratively. The student's actions or choices provide inputs to the model which affect the subsequent values and states of different quantities within the model. By this means, activities, causes, and effects within a scenario can be realistically simulated. With quantitative model-based simulations, the number of possible states can be effectively infinite. The present invention is only concerned with this latter type of educational simulation.
Quantitative domain models are commonly used in research and for forecasting, in domains ranging from economics and business, to chemistry, biology, and medicine, to mechanical and electronic systems. But their usability for educational purposes is limited. Except when the number of variables in a model is very small or when there is little interaction between different entities in a model, these simulation models are overwhelming to typical students. Moreover, the user interfaces for the models typically also lack user-friendliness.
Yet their potential for teaching students about the structure, characteristics and behavior of complex systems is very great. Domain models embody highly-refined knowledge in an explicit and illuminating form. Students could become engaged in experimentation, trying their own hypotheses, and watching firsthand as the interplay between different entities unfolds.
There are several difficulties students face when interacting with a quantitative model-based simulation. Students may not be able to relate bare numeric outcomes to relevant theoretical concepts. Students also typically do not know what many of the quantities or variables in the model mean, or what is their significance, or specifically which other quantities affect them. Students are also prone to cognitive overload as they try to analyze the effects of several different inputs to the simulation model.
Each of these difficulties ruins the students' experience and/or causes them to become discouraged. The difficulty of creating interactive environments that overcome these challenges for students has prevented the proliferation of this potentially-valuable educational approach. It's not only that development of quantitative model-based simulations is complicated and expensive, and requires a great deal of expertise, and that existing development tools are inadequate. More importantly, it is that there has been a lack of ideas and design strategies for enabling students to effectively deal with the complexity in such simulations.